题意：我无法将聊天机器人模型 chatbot 与React连接起来

问题背景：

This is the model

from flask import Flask, request, jsonify
from flask_cors import CORS
import json
import nltk
import numpy as np
import random
import pickle
from time import sleep
from nltk.stem.lancaster import LancasterStemmer
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from pathlib import Path
from pyngrok import ngrok

stemmer = LancasterStemmer()

# Load intents file
with open("intents2.json") as file:
    data = json.load(file)
    
app = Flask(__name__)
CORS(app)  # Enable CORS for all routes    

# Load preprocessed data or process it if not available
try:
    with open("data.pickle", "rb") as f:
        words, labels, training, output = pickle.load(f)
except:
    words = []
    labels = []
    docs_x = []
    docs_y = []

    for intent in data["intents"]:
        for pattern in intent["patterns"]:
            wrds = nltk.word_tokenize(pattern)
            words.extend(wrds)
            docs_x.append(wrds)
            docs_y.append(intent["tag"])

        if intent["tag"] not in labels:
            labels.append(intent["tag"])

    words = [stemmer.stem(w.lower()) for w in words if w != "?"]
    words = sorted(list(set(words)))

    labels = sorted(labels)

    training = []
    output = []

    out_empty = [0 for _ in range(len(labels))]

    for x, doc in enumerate(docs_x):
        bag = []

        wrds = [stemmer.stem(w) for w in doc]

        for w in words:
            if w in wrds:
                bag.append(1)
            else:
                bag.append(0)

        output_row = out_empty[:]
        output_row[labels.index(docs_y[x])] = 1

        training.append(bag)
        output.append(output_row)

    training = np.array(training)
    output = np.array(output)

    with open("data.pickle", "wb") as f:
        pickle.dump((words, labels, training, output), f)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(training, output, test_size=0.2)

# Define the Keras model
model = Sequential()
model.add(Dense(20, input_shape=(len(X_train[0]),), activation='relu'))
model.add(Dense(20, activation='relu'))
model.add(Dense(20, activation='relu'))
model.add(Dense(len(y_train[0]), activation='softmax'))

# Compile the model
model.compile(optimizer=Adam(learning_rate=0.01), loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model
model.fit(X_train, y_train, epochs=200, batch_size=8, verbose=1)

# Save the model
model.save("model.h5")

# Evaluate the model
y_train_pred = model.predict(X_train)
f1_train = f1_score(y_train.argmax(axis=1), y_train_pred.argmax(axis=1), average='weighted')
print(f"F1 Score (Training): {f1_train:.4f}")

test_loss, test_acc = model.evaluate(X_test, y_test)
print(f"Test Loss: {test_loss:.4f}")
print(f"Test Accuracy: {test_acc:.4f}")

def bag_of_words(s, words):
    bag = [0 for _ in range(len(words))]

    s_words = nltk.word_tokenize(s)
    s_words = [stemmer.stem(word.lower()) for word in s_words]

    for se in s_words:
        for i, w in enumerate(words):
            if w == se:
                bag[i] = 1

    return np.array(bag)

@app.route('/chat', methods=['POST'])
def chat():
    user_input = request.form.get('message')
    if not user_input:
        return jsonify({"error": "No message provided"}), 400
    
    #print("Hi, How can I help you?")
    #while True:
     #   inp = input("You: ")
      #  if inp.lower() == "quit":
       #     break

    results = model.predict(np.array([bag_of_words(user_input, words)]))[0]
    results_index = np.argmax(results)
    tag = labels[results_index]
        
    if results[results_index] > 0.8:
        for tg in data["intents"]:
            if tg['tag'] == tag:
                responses = tg['responses']
        sleep(1)
        bot_response = random.choice(responses)
    else:
        bot_response = "I don't understand!"

    return jsonify({"response": bot_response})    
if __name__ == '__main__':
    # Set up ngrok
    ngrok.set_auth_token("2i16Qv3WbGVeggfwzrzUGEZbEK5_3d5kArxvbSNbwB6kQsJZA")
    public_url = ngrok.connect(5000).public_url
    print(" * ngrok tunnel \"{}\" -> \"http://127.0.0.1:5000/\"".format(public_url))

    # Run Flask app
    app.run(debug=True, use_reloader=False)

#chat()

this is the error i got while i tried to see the output i will get

I made AI model that answer your questions and in work well in python so i used flask to link it to front end i got the api link and try it on postman and it goes well but i can't link it with react

问题解决：

In your flask Code, at the top of your Chat Route you have:

user_input = request.form.get("message")

But in the flask Api, the form.get only works If the Data is NOT JSON. But INSIDE your fetch call in the react code, you JSON stringify your Message Object. Now the form.get function cannot get the Message. If you deleted the JSON.stringify INSIDE your Body, It should Work then.